// Small bench routine for Eigen available in Eigen
// (C) Desire NUENTSA WAKAM, INRIA

#include <Eigen/Householder>
#include <Eigen/IterativeLinearSolvers>
#include <Eigen/Jacobi>
#include <Eigen/LU>
#include <fstream>
#include <iomanip>
#include <iostream>
#include <unsupported/Eigen/SparseExtra>
// #include <Eigen/SparseLU>
#include <Eigen/SuperLUSupport>
// #include <unsupported/Eigen/src/IterativeSolvers/Scaling.h>
#include <bench/BenchTimer.h>
#include <unsupported/Eigen/IterativeSolvers>
using namespace std;
using namespace Eigen;

int
main(int argc, char** args)
{
	SparseMatrix<double, ColMajor> A;
	typedef SparseMatrix<double, ColMajor>::Index Index;
	typedef Matrix<double, Dynamic, Dynamic> DenseMatrix;
	typedef Matrix<double, Dynamic, 1> DenseRhs;
	VectorXd b, x, tmp;
	BenchTimer timer, totaltime;
	// SparseLU<SparseMatrix<double, ColMajor> >   solver;
	//   SuperLU<SparseMatrix<double, ColMajor> >   solver;
	ConjugateGradient<SparseMatrix<double, ColMajor>, Lower, IncompleteCholesky<double, Lower>> solver;
	ifstream matrix_file;
	string line;
	int n;
	// Set parameters
	//   solver.iparm(IPARM_THREAD_NBR) = 4;
	/* Fill the matrix with sparse matrix stored in Matrix-Market coordinate column-oriented format */
	if (argc < 2)
		assert(false && "please, give the matrix market file ");

	timer.start();
	totaltime.start();
	loadMarket(A, args[1]);
	cout << "End charging matrix " << endl;
	bool iscomplex = false, isvector = false;
	int sym;
	getMarketHeader(args[1], sym, iscomplex, isvector);
	if (iscomplex) {
		cout << " Not for complex matrices \n";
		return -1;
	}
	if (isvector) {
		cout << "The provided file is not a matrix file\n";
		return -1;
	}
	if (sym != 0) { // symmetric matrices, only the lower part is stored
		SparseMatrix<double, ColMajor> temp;
		temp = A;
		A = temp.selfadjointView<Lower>();
	}
	timer.stop();

	n = A.cols();
	// ====== TESTS FOR SPARSE TUTORIAL ======
	//   cout<< "OuterSize " << A.outerSize() << " inner " << A.innerSize() << endl;
	//   SparseMatrix<double, RowMajor> mat1(A);
	//   SparseMatrix<double, RowMajor> mat2;
	//   cout << " norm of A " << mat1.norm() << endl; ;
	//   PermutationMatrix<Dynamic, Dynamic, int> perm(n);
	//   perm.resize(n,1);
	//   perm.indices().setLinSpaced(n, 0, n-1);
	//   mat2 = perm * mat1;
	//   mat.subrows();
	//   mat2.resize(n,n);
	//   mat2.reserve(10);
	//   mat2.setConstant();
	//   std::cout<< "NORM " << mat1.squaredNorm()<< endl;

	cout << "Time to load the matrix " << timer.value() << endl;
	/* Fill the right hand side */

	//   solver.set_restart(374);
	if (argc > 2)
		loadMarketVector(b, args[2]);
	else {
		b.resize(n);
		tmp.resize(n);
		//       tmp.setRandom();
		for (int i = 0; i < n; i++)
			tmp(i) = i;
		b = A * tmp;
	}
	//   Scaling<SparseMatrix<double> > scal;
	//   scal.computeRef(A);
	//   b = scal.LeftScaling().cwiseProduct(b);

	/* Compute the factorization */
	cout << "Starting the factorization " << endl;
	timer.reset();
	timer.start();
	cout << "Size of Input Matrix " << b.size() << "\n\n";
	cout << "Rows and columns " << A.rows() << " " << A.cols() << "\n";
	solver.compute(A);
	//   solver.analyzePattern(A);
	//   solver.factorize(A);
	if (solver.info() != Success) {
		std::cout << "The solver failed \n";
		return -1;
	}
	timer.stop();
	float time_comp = timer.value();
	cout << " Compute Time " << time_comp << endl;

	timer.reset();
	timer.start();
	x = solver.solve(b);
	//   x = scal.RightScaling().cwiseProduct(x);
	timer.stop();
	float time_solve = timer.value();
	cout << " Time to solve " << time_solve << endl;

	/* Check the accuracy */
	VectorXd tmp2 = b - A * x;
	double tempNorm = tmp2.norm() / b.norm();
	cout << "Relative norm of the computed solution : " << tempNorm << "\n";
	//   cout << "Iterations : " << solver.iterations() << "\n";

	totaltime.stop();
	cout << "Total time " << totaltime.value() << "\n";
	//  std::cout<<x.transpose()<<"\n";

	return 0;
}
